CN112182916A - Power distribution network reliability marginal benefit and marginal cost analysis method and system - Google Patents
Power distribution network reliability marginal benefit and marginal cost analysis method and system Download PDFInfo
- Publication number
- CN112182916A CN112182916A CN202011217642.9A CN202011217642A CN112182916A CN 112182916 A CN112182916 A CN 112182916A CN 202011217642 A CN202011217642 A CN 202011217642A CN 112182916 A CN112182916 A CN 112182916A
- Authority
- CN
- China
- Prior art keywords
- power
- reliability
- distribution network
- marginal
- loss
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000008901 benefit Effects 0.000 title claims abstract description 65
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000007728 cost analysis Methods 0.000 title claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 45
- 238000004458 analytical method Methods 0.000 claims abstract description 31
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 230000035945 sensitivity Effects 0.000 claims abstract description 9
- 238000013210 evaluation model Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000006870 function Effects 0.000 claims description 30
- 238000007619 statistical method Methods 0.000 claims description 12
- 238000010206 sensitivity analysis Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a method and a system for analyzing reliability marginal benefit and marginal cost of a power distribution network, wherein the method comprises the following steps: s1, receiving a reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network; s2, processing the big data to determine the reliability weak link of the power distribution network; s3, changing reliability parameters based on the weak link of the power distribution network, performing predictive evaluation by applying a power distribution network reliability evaluation model, and analyzing marginal benefit/cost sensitivity; s4: and outputting and displaying the analysis result. The invention reduces the reliability optimization cost, improves the reliability level, has more accurate analysis result and meets the actual requirement.
Description
Technical Field
The invention belongs to the field of power system automation, and particularly relates to a method and a system for analyzing reliability marginal benefit and marginal cost of a power distribution network.
Background
The reliability of the power distribution system is characterized in that on the basis of an already-operated power distribution network and a newly-designed power distribution network, evaluation analysis is carried out on the power supply reliability of all line equipment, the influence on the power supply reliability is determined, technical measures for improving the power supply reliability are determined according to the influence, and a management method for improving the power supply reliability is sought. The improvement of the reliability of the power distribution system is an important goal of smart grid construction, and due to the influence of various factors, the investment behaviors performed for improving the power supply reliability of the power distribution network are particularly in great risk, so that the marginal benefit and the marginal cost of the reliability need to be analyzed, so that the analysis result is more accurate, and the actual requirements are met.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art and provide a method and a system for analyzing reliability marginal benefit and marginal cost of a power distribution network.
According to one aspect of the invention, the invention provides a method for analyzing reliability marginal benefit and marginal cost of a power distribution network, which comprises the following steps:
s1, receiving a reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
s2, processing the big data to determine the reliability weak link of the power distribution network;
s3, changing reliability parameters based on the weak link of the power distribution network, performing predictive evaluation by applying a power distribution network reliability evaluation model, and analyzing marginal benefit/cost sensitivity;
s4: and outputting and displaying the analysis result.
Preferably, the big data is data associated with at least one power supply area or distribution line; the reliability weak link of the power distribution network comprises statistical analysis of an actual power failure event, wherein the statistical analysis of the actual power failure event is analyzed by adopting a user power failure loss evaluation rate.
Preferably, the user power failure loss evaluation rate is calculated by adopting the following steps:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuRepresents the down time in the u-th power failure, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a function of the integrated customer outage loss at load point i, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
Preferably, the marginal benefit/cost sensitivity analysis result is utilized to decompose the reliability marginal cost curve into a plurality of curves according to different reliability optimization measures, each curve represents the reliability marginal cost when the measure is independently used to reach the maximum reliability level, and the plurality of curves are output and displayed.
According to another aspect of the present invention, the present invention further provides a system for analyzing reliability marginal benefit and marginal cost of a power distribution network, the system comprising:
the receiving module is used for receiving the reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
the determining module is used for processing the big data and determining the reliability weak link of the power distribution network;
the analysis module is used for changing reliability parameters based on the weak link of the power distribution network, applying a power distribution network reliability evaluation model to carry out predictive evaluation and analyzing marginal benefit/cost sensitivity;
and the output module is used for outputting and displaying the analysis result.
Preferably, the big data is data associated with at least one power supply area or distribution line; the reliability weak link of the power distribution network comprises statistical analysis of an actual power failure event, wherein the statistical analysis of the actual power failure event is analyzed by adopting a user power failure loss evaluation rate.
Preferably, the user power failure loss evaluation rate is calculated by adopting the following steps:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuRepresents the down time in the u-th power failure, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a function of the integrated customer outage loss at load point i, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
Preferably, the marginal benefit/cost sensitivity analysis result is utilized to decompose the reliability marginal cost curve into a plurality of curves according to different reliability optimization measures, each curve represents the reliability marginal cost when the measure is independently used to reach the maximum reliability level, and the plurality of curves are output and displayed.
According to another aspect of the present invention, the present invention further provides a system for analyzing reliability marginal benefit and marginal cost of a power distribution network, the system comprising: a processor, a memory, said memory storing computer executable instructions which, when executed by the processor, implement the above-mentioned method steps.
According to another aspect of the present invention, there is also provided a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions which, when executed by a processor, implement the above-mentioned method steps.
Has the advantages that: according to the method, on the basis of obtaining the big data for analyzing the marginal cost benefit of the reliability of the power distribution network, the weak links of the reliability influence factors are analyzed, the marginal cost curve of the reliability is decomposed according to different optimization measures, the reliability optimization cost is reduced, the reliability level is improved, the analysis result is more accurate, and the method meets the actual requirement.
The features and advantages of the present invention will become apparent by reference to the following drawings and detailed description of specific embodiments of the invention.
Drawings
FIG. 1 is a flow chart of a method for analyzing reliability marginal benefit and marginal cost of a power distribution network;
FIG. 2 is a schematic diagram of a system for analyzing marginal benefit and marginal cost of reliability of a power distribution network;
FIG. 3 is a schematic diagram of another power distribution network reliability marginal benefit and marginal cost analysis system.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
FIG. 1 is a flow chart of a method for analyzing reliability marginal benefit and marginal cost of a power distribution network. As shown in fig. 1, the present invention provides a method for analyzing reliability marginal benefit and marginal cost of a power distribution network, wherein the method comprises the following steps:
s1, receiving a reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
in the step, the analysis system receives a reliability marginal benefit and marginal cost analysis request input by a manager, and responds to the analysis request to obtain big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network. Wherein the big data is data associated with at least one power supply area or distribution line. The marginal cost of reliability is: increased investment costs are required to increase unit reliability levels; the marginal benefit of reliability is: the benefits obtained by increasing the unit reliability level or the loss of power outage reduced thereby.
And S2, processing the big data and determining the reliability weak link of the power distribution network.
Processing the big data, and clearing the influence of various data on different aspects of reliability and cost benefit of the power distribution network; by analyzing the influences, it can be known that in the same area, different reliability optimization measures have different investments in improving unit reliability and different generated benefits, so that the reliability marginal cost curve is decomposed according to different optimization measures, the influences of the different optimization measures on the reliability can be more accurately known, and therefore the most economical and reasonable optimization scheme adopted for realizing a certain set reliability is obtained, and lean control of the planning cost benefit of the power distribution network is realized.
And (3) clearing influences of various types of data on different aspects of reliability and cost benefits of the power distribution network so as to clearly and clearly research the reliability weak links of the object.
Preferably, the reliability weak link comprises a statistical analysis of an actual power outage event; the statistical analysis of the actual power failure event adopts a user power failure loss evaluation rate for analysis, and the user power failure loss evaluation rate is calculated by adopting the following steps:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuRepresents the down time in the u-th power failure, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a general user outage at load point iLoss function, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
And S3, changing reliability parameters based on the weak link of the power distribution network, performing predictive evaluation by applying a power distribution network reliability evaluation model, and analyzing marginal benefit/cost sensitivity.
Specifically, the influence on the reliability of each type of measure is converted into the change on the reliability parameter, and then the change is applied to a reliability evaluation model of the power distribution network for predictive evaluation, so that the reliability improvement effect of the measure after being implemented independently is obtained, the ratio of the marginal benefit to the marginal cost is benefit/cost sensitivity, and the higher the value is, the higher the sensitivity of the measure is.
S4: and outputting and displaying the analysis result.
After the analysis is completed, the system outputs and displays the analysis result, specifically, the analysis result can be displayed through a human-computer interface, and the analysis result can also be broadcasted through a voice broadcasting mode.
Preferably, the marginal benefit/cost sensitivity analysis result is utilized to decompose the reliability marginal cost curve into a plurality of curves according to different reliability optimization measures, each curve represents the reliability marginal cost when the measure is independently used to reach the maximum reliability level, and the plurality of curves are output and displayed.
And decomposing the reliability marginal cost curve into a plurality of curves according to different reliability optimization measures by utilizing the benefit/cost sensitivity analysis result, wherein each curve represents the reliability marginal cost when the measure is independently used to reach the maximum reliability level. Because the benefit of improving the unit reliability rate in the given regional power distribution network is a fixed value, the reliability marginal benefit curves corresponding to a plurality of measures can be drawn up to be a curve, so that the analysis process is simplified, and a foundation is laid for lean analysis of various types of partitions under different reliability optimization measures.
According to the method, on the basis of obtaining the big data for analyzing the marginal cost benefit of the reliability of the power distribution network, the weak links of the reliability influence factors are analyzed, the marginal cost curve of the reliability is decomposed according to different optimization measures, the reliability optimization cost is reduced, the reliability level is improved, the analysis result is more accurate, and the method meets the actual requirement.
Example 2
FIG. 2 is a schematic diagram of a system for analyzing marginal benefit and marginal cost of reliability of a power distribution network. As shown in fig. 2, the present invention further provides a system for analyzing marginal benefit and marginal cost of reliability of a power distribution network, where the system includes:
the receiving module is used for receiving the reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
the determining module is used for processing the big data and determining the reliability weak link of the power distribution network;
the analysis module is used for changing reliability parameters based on the weak link of the power distribution network, applying a power distribution network reliability evaluation model to carry out predictive evaluation and analyzing marginal benefit/cost sensitivity;
and the output module is used for outputting and displaying the analysis result.
Preferably, the big data is data associated with at least one power supply area or distribution line; the reliability weak link of the power distribution network comprises statistical analysis of an actual power failure event, wherein the statistical analysis of the actual power failure event is analyzed by adopting a user power failure loss evaluation rate.
Preferably, the user power failure loss evaluation rate is calculated by adopting the following steps:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuRepresents the down time in the u-th power failure, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a function of the integrated customer outage loss at load point i, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
Preferably, the marginal benefit/cost sensitivity analysis result is utilized to decompose the reliability marginal cost curve into a plurality of curves according to different reliability optimization measures, each curve represents the reliability marginal cost when the measure is independently used to reach the maximum reliability level, and the plurality of curves are output and displayed.
The specific implementation process of the method steps executed by each module in embodiment 2 of the present invention is the same as the implementation process of each step in embodiment 1, and is not described herein again.
Example 3
FIG. 3 is a schematic diagram of another power distribution network reliability marginal benefit and marginal cost analysis system. As shown in fig. 3, the present invention further provides a system for analyzing marginal benefit and marginal cost of reliability of a power distribution network, where the system includes: the processor and the memory, where the memory stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method steps in embodiment 1, and a specific implementation process may refer to an implementation process of the method steps in embodiment 1, which is not described herein again.
Example 4
According to another aspect of the present invention, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions, when executed by a processor, implement the method steps in embodiment 1, and for a specific implementation process, reference may be made to an implementation process of the method steps in embodiment 1, which is not described herein again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for analyzing reliability marginal benefit and marginal cost of a power distribution network is characterized by comprising the following steps:
s1, receiving a reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
s2, processing the big data to determine the reliability weak link of the power distribution network;
s3, changing reliability parameters based on the weak link of the power distribution network, performing predictive evaluation by applying a power distribution network reliability evaluation model, and analyzing marginal benefit/cost sensitivity;
s4: and outputting and displaying the analysis result.
2. The method of claim 1, wherein the big data is data associated with at least one power supply area or distribution line; the reliability weak link of the power distribution network comprises statistical analysis of an actual power failure event, wherein the statistical analysis of the actual power failure event is analyzed by adopting a user power failure loss evaluation rate.
3. The method of claim 2, wherein the user power outage loss evaluation rate is calculated by:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuRepresents the down time in the u-th power failure, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a function of the integrated customer outage loss at load point i, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
4. The method of claim 1, wherein the reliability marginal cost curve is decomposed into a plurality of curves according to different reliability optimization measures using the marginal benefit/cost sensitivity analysis results, each curve representing the reliability marginal cost for achieving its maximum reliability level when the measure is used alone, and the plurality of curves are output and displayed.
5. A power distribution network reliability marginal benefit and marginal cost analysis system, the system comprising:
the receiving module is used for receiving the reliability marginal benefit and marginal cost analysis request, responding to the analysis request, and acquiring big data for analyzing the reliability marginal benefit and marginal cost of the power distribution network;
the determining module is used for processing the big data and determining the reliability weak link of the power distribution network;
the analysis module is used for changing reliability parameters based on the weak link of the power distribution network, applying a power distribution network reliability evaluation model to carry out predictive evaluation and analyzing marginal benefit/cost sensitivity;
and the output module is used for outputting and displaying the analysis result.
6. The system of claim 5, wherein the big data is data associated with at least one power supply area or distribution line; the reliability weak link of the power distribution network comprises statistical analysis of an actual power failure event, wherein the statistical analysis of the actual power failure event is analyzed by adopting a user power failure loss evaluation rate.
7. The system of claim 6, wherein the user power outage loss evaluation rate is calculated by:
(1) firstly, solving a power failure loss function of classified users to represent the relationship between power failure loss and power failure time of the users;
(2) calculating a comprehensive user loss function, taking the proportion of the power consumption of various users at a certain node as a weight coefficient, and calculating a weighted average value of the power failure loss functions of various users; the integrated user loss function at a certain point i is:
wherein E represents the number of user classes on a certain node i, PjIndicating the average load value, SCDF, of class j usersj(t) represents the loss of j class users when power is off t;
(3) firstly, the power shortage loss evaluation rate R of each load node is calculated1Under the comprehensive consideration of the influence of the power shortage, the power shortage duration, the power shortage frequency and the comprehensive power shortage loss of the user, the unit power shortage loss evaluation rate at the node is calculated by the following formula:
wherein W represents the total number of power failures, AiuIndicates the fault rate in the u-th power failure, riuDenotes the u-th orderOutage time in power outage, LiRepresents the average load at a certain node i; c (r)iu) Expressed as a function of the integrated customer outage loss at load point i, C (r)iu)＝CCDFiChanges with different power failure time;
repeating the step (3) until the power shortage loss evaluation rates of all the load nodes are calculated, and calculating the power outage loss evaluation rate R of the whole system2；
And N is the number of load nodes of the power distribution network.
8. The system of claim 5, wherein the reliability marginal cost curve is decomposed into a plurality of curves according to different reliability optimization measures using the marginal benefit/cost sensitivity analysis results, each curve representing the reliability marginal cost for achieving its maximum reliability level when the measure is used alone, and the plurality of curves are output and displayed.
9. A power distribution network reliability marginal benefit and marginal cost analysis system, the system comprising: a processor, a memory storing computer-executable instructions that, when executed by the processor, implement the method of any one of claims 1-4.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011217642.9A CN112182916A (en) | 2020-11-04 | 2020-11-04 | Power distribution network reliability marginal benefit and marginal cost analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011217642.9A CN112182916A (en) | 2020-11-04 | 2020-11-04 | Power distribution network reliability marginal benefit and marginal cost analysis method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112182916A true CN112182916A (en) | 2021-01-05 |
Family
ID=73917719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011217642.9A Pending CN112182916A (en) | 2020-11-04 | 2020-11-04 | Power distribution network reliability marginal benefit and marginal cost analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112182916A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102013051A (en) * | 2010-10-26 | 2011-04-13 | 中国电力科学研究院 | Method for analyzing and optimizing reliability cost/benefit precision of power supply |
| CN102306346A (en) * | 2011-08-12 | 2012-01-04 | 中国电力科学研究院 | Decision method for optimizing objective net support structure of medium-voltage distribution network based on reliability program |
| CA2849185A1 (en) * | 2013-04-30 | 2014-10-30 | General Electric Company | Systems and methods for estimating reliability return on utility vegetation management |
| CN107480862A (en) * | 2017-07-19 | 2017-12-15 | 天津大学 | A kind of Distribution Network Reliability marginal cost marginal analysis method based on big data |
-
2020
- 2020-11-04 CN CN202011217642.9A patent/CN112182916A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102013051A (en) * | 2010-10-26 | 2011-04-13 | 中国电力科学研究院 | Method for analyzing and optimizing reliability cost/benefit precision of power supply |
| CN102306346A (en) * | 2011-08-12 | 2012-01-04 | 中国电力科学研究院 | Decision method for optimizing objective net support structure of medium-voltage distribution network based on reliability program |
| CA2849185A1 (en) * | 2013-04-30 | 2014-10-30 | General Electric Company | Systems and methods for estimating reliability return on utility vegetation management |
| CN107480862A (en) * | 2017-07-19 | 2017-12-15 | 天津大学 | A kind of Distribution Network Reliability marginal cost marginal analysis method based on big data |
Non-Patent Citations (2)
| Title |
|---|
| A. PAPAEMMANOUIL; L. BERTLING; L. A. TUAN; G. ANDERSSON: "Improved cost-benefit analysis for reliable long-term transmission planning", 2011 IEEE TRONDHEIM POWERTECH, pages 1 - 6 * |
| 王晶: "基于可靠性成本—效益分析的配电网规划优化研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 4, pages 2 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| De Menezes et al. | Review of guidelines for the use of combined forecasts | |
| Sou et al. | Computing critical $ k $-tuples in power networks | |
| CN103702401B (en) | Mobile network's power-economizing method based on user behavior analysis | |
| Azizi et al. | Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units | |
| CN105225026A (en) | A kind of Research on Housing Vacancy Rate appraisal procedure based on electric energy service management platform | |
| CN102013051A (en) | Method for analyzing and optimizing reliability cost/benefit precision of power supply | |
| CN113469585A (en) | Carbon management data adjusting method, carbon management platform, system and storage medium | |
| CN111563077A (en) | Power grid voltage data missing filling method, system, terminal and storage medium | |
| CN112308270A (en) | Long-term electricity load prediction method and device and computer implementation system | |
| CN113268403A (en) | Time series analysis and prediction method, device, equipment and storage medium | |
| CN113191828B (en) | User electricity price value grade label construction method, device, equipment and medium | |
| US20100251204A1 (en) | System and method for determining software test cycle effectiveness | |
| De Crombrugghe et al. | Statistical Demand Functions for Food in the USA and the Netherlands | |
| CN109635996A (en) | Room rate tendency look-forward model construction method, device and storage medium | |
| CN112182916A (en) | Power distribution network reliability marginal benefit and marginal cost analysis method and system | |
| CN116249186A (en) | Data processing method and device of wireless network equipment, storage medium and electronic equipment | |
| US20220243347A1 (en) | Determination method and determination apparatus for conversion efficiency of hydrogen production by wind-solar hybrid electrolysis of water | |
| CN113837486B (en) | RNN-RBM-based distribution network feeder long-term load prediction method | |
| CN114995964A (en) | Combination service reconstruction method, device, equipment and computer readable medium | |
| CN105743720A (en) | Link quality assessment method and device | |
| CN115330219A (en) | Resource scheduling method and device | |
| CN114759579A (en) | Power grid active power optimization control system, method and medium based on data driving | |
| CN114860563A (en) | Application program testing method and device, computer readable storage medium and equipment | |
| CN115063255B (en) | Operation and maintenance resource optimal allocation method, device and system for offshore wind farm | |
| CN104598362B (en) | Data center power allocation method based on application performance loss analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |